Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.

Background: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could help tailor treatments to each patient's needs.

Objective: We sought to discern the individual suitability of radiotherapy (RT) or chemoradiotherapy (CRT) in LGG patients using ML models.

Methods: Ten ML models, trained to infer ITE in 4,042 LGG patients, were assessed. We compared patients who followed treatment recommendations provided by the models with those who did not. To mitigate the risk of treatment selection bias, we employed inverse probability treatment weighting (IPTW).

Results: The Balanced Survival Lasso-Network (BSL) model showed the most significant protective effect among all the models we tested (hazard ratio (HR): 0.52, 95% CI, 0.41-0.64; IPTW-adjusted HR: 0.58, 95% CI, 0.45-0.74; the difference in restricted mean survival time (DRMST): 9.11, 95% CI, 6.19-12.03; IPTW-adjusted DRMST: 9.17, 95% CI, 6.30-11.83). CRT presented a protective effect in the 'recommend for CRT' group (IPTW-adjusted HR: 0.60, 95% CI, 0.39-0.93) yet presented an adverse effect in the 'recommend for RT' group (IPTW-adjusted HR: 1.64, 95% CI, 1.19-2.25). Moreover, the models predict that younger patients and patients with overlapping lesions or tumors crossing the midline are better suited for CRT (HR: 0.62, 95% CI, 0.42-0.91; IPTW-adjusted HR: 0.59, 95% CI, 0.36-0.97).

Conclusion: Our findings underscore the potential of the BSL model in guiding the choice of adjuvant treatment for LGGs patients, potentially improving survival time. This study emphasizes the importance of ML in customizing patient care, understanding the nuances of treatment selection, and advancing personalized medicine.